Secure communications methods for use with entrepreneurial prediction systems and methods

ABSTRACT

Secure communications methods for use with entrepreneurial prediction systems and methods are provided herein. An example method can include a two factor authentication of both a communications channel used by the entrepreneur (either by device or message attributes) and an identification of an identity of the entrepreneur from biometric parameters. This allows for secure communication with an entrepreneur when the entrepreneur is communicating from a geographical location of low trust, such as where device or identity theft is common.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. application Ser. No.14/671,868, filed on Mar. 27, 2015, titled “Multi-Variable AssessmentSystems and Methods that Evaluate and Predict Entrepreneurial Behavior”which claims the priority benefit of U.S. Application Ser. No.61/973,209, filed on Mar. 31, 2014, titled “Systems and Methods forEntrepreneurial Prediction,” all of which are hereby incorporated byreference herein in their entirety, including all references citedtherein.

FIELD OF THE INVENTION

The present technology pertains to the field of behavior scoring andprediction, and more particularly to a multi-variable assessment systemthat determines scores or measures relating to the likelihood of variousbusiness-related outcomes.

SUMMARY

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions. Onegeneral aspect includes a method for verifying secure communicationsbetween a sending party and a receiving party over a communicationsnetwork, the method comprising: (a) determining if the sending orreceiving parties are accessing the communications network from ageographical location of low trust; (b) verifying a communicationschannel of the communications network by: (i) authenticatingcommunications devices used by the sending party and the receivingparty; or (ii) authenticating a message transmitted from the sendingparty to the receiving party using an encryption scheme and an errordetection block associated with the message; and (c) verifying thesending party by: (i) receiving current biometric information for thesending party; (ii) authenticating the current biometric informationagainst pre-stored biometric information for the sending party toauthenticate or reject the transmitted message; and (d) transmitting themessage to the receiving party if the communications channel and thesending party are verified.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

One general aspect includes a method including (a) determining if thesending or receiving parties are accessing the communications networkfrom a geographical location of low trust; (b) verifying acommunications channel of the communications network by: (i)authenticating communications devices used by the sending party and thereceiving party; or (ii) authenticating a message transmitted from thesending party to the receiving party using an encryption scheme and anerror detection block associated with the message; and (c) verifying thesending party by: (i) receiving current biometric information for thesending party; (ii) authenticating the current biometric informationagainst pre-stored biometric information for the sending party toauthenticate or reject the transmitted message; (iii) extractingmeta-data from the current biometric information; and (iv)authenticating the meta-data of the current biometric informationagainst pre-stored meta-data for the sending party to authenticate orreject the transmitted message; and (d) transmitting the message to thereceiving party if the communications channel and the sending party areverified.

One general aspect includes a method including: (a) pre-storingentrepreneur data that comprises location information and biometricinformation for an entrepreneur, as well as identifying information fora communications device used by the entrepreneur to access thecommunications network; (b) receiving a message transmitted from theentrepreneur using their communications device to a receiving party; (c)verifying a communications channel of the communications network used bythe entrepreneur by: (i) authenticating the communications device usedby the entrepreneur; and (d) verifying an identity of the entrepreneurby: (i) receiving current biometric information for the entrepreneur;and (ii) authenticating the current biometric information against thepre-stored biometric information for the entrepreneur to authenticate orreject the transmitted message; and (e) encrypting the message andassociating the message with an error detection block; and (f)transmitting the message to the receiving party if the communicationschannel and the identity of the entrepreneur are verified.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, and serve to further illustrateembodiments of concepts that include the claimed disclosure, and explainvarious principles and advantages of those embodiments.

The methods and systems disclosed herein have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present disclosure so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

FIG. 1 is a schematic diagram of a process for receiving various sourcesof information, extracting relevant information and translating theextracted information so that it can be stored in data stores relatingto attributes of either the entrepreneur, the business opportunity or tothe social network and social capital of the entrepreneur.

FIG. 2 is a diagram of a process for extracting features from thecategorized databases, providing these features to predictive models(either mathematically derived or qualitatively derived), which thenproduces scores relating to the entrepreneurial success in question.

FIG. 3 illustrates a scoring model with multiple idealized clusters ofbehavior, for use in accordance with the present technology.

FIG. 4 is a schematic diagram of an exemplary computing architecturethat can be used to practice aspects of the present technology.

FIG. 5 is a flowchart of an example method of the present technology.

FIG. 6 is a flow diagram of an example feature extraction process, wherefeatures are used to validate a transaction, and preferably in someembodiments on an ongoing basis during the transaction.

FIG. 7 is a flowchart of an example method for performing a multi-locimodeling of an individual to determine their entrepreneurial ability.

FIG. 8 is an example flow diagram of a data collection and analysisprocess of the present technology.

FIG. 9 illustrates an exemplary computing system that may be used toimplement embodiments according to the present technology.

FIG. 10 is a schematic diagram of an example system forauthenticating/verifying communications received one or more parties.

FIG. 11 is a flowchart of an example method for verifying securecommunications between a sending party and a receiving party over acommunications network.

FIG. 12 is a flowchart of a method for verifying secure communicationsover a communications network.

DETAILED DESCRIPTION

While this technology is susceptible of embodiment in many differentforms, there is shown in the drawings and will herein be described indetail several specific embodiments with the understanding that thepresent disclosure is to be considered as an exemplification of theprinciples of the technology and is not intended to limit the technologyto the embodiments illustrated.

The present technology pertains to the field of behavior scoring andprediction, and more particularly to multi-variable assessment methodsand processes that determine scores or metrics relating to thelikelihood of various business-related outcomes.

For example, some assessment scores, which serve as predictors of bothspecific behaviors and of general capabilities are known in the art.Such systems allow for the assignment of scores relating to creditworthiness (or purchasing likelihood, or next click in web browsingbehavior) or the likelihood of other very specific behaviors. Somescores assess general capabilities such as intelligence, but thesescores tend to be either very specific to a single feature relating toan individual, or are very general relating to a global attribute suchas intelligence.

Additionally, behavior scores relating to repayments due undercontracts, such as credit scores, rely upon centralized stores ofverified information about previously demonstrated behavior.

In accordance with the present technology, a multi-variable system andmethod are provided that allow for the scoring of a complex set ofinputs, together with information associated with social-networkstructure and activity of an individual. These diverse types ofinformation are coalesced by the present technology to assess theentrepreneurial behavior of an individual. This technology solves theknown problem of predicting entrepreneurial success—which may for thepurpose of this description be defined as predicting the likelihood of abusiness person successfully conducting one or more businesstransactions and subsequently repaying investment capital that may havebeen advanced for that business purpose.

To be sure, the present technology calculates a plurality of unique andproprietary scores and indications that allow for the assessment ofentrepreneurial ability of an individual. This assessment can beutilized to determine the suitability of the individual for a businessopportunity or as an informative tool that allows the individual toassess their entrepreneurial ability as compared to other individuals.

The problem of predicting entrepreneurial success, including repayment,is often exacerbated by having little or no verifiable information aboutthe previous credit history of the entrepreneur. This problem is alsofurther exacerbated by many jurisdictions having no central source forverification of income and payment history of the entrepreneur's pastperformance. Furthermore, the current technology incorporates within itsscoring methodology the view that the legal system in which theentrepreneur operates is either ineffective or provides an impracticalenforcement mechanism for encouraging contract adherence by theentrepreneur, either due to the uncertainty within that legal system orbecause of the impracticality of pursuing legal remedies due to theexpense of such remedy relative to the investment capital hoping to berecovered.

The present technology and scoring system is neither based upon a singlebehavior, nor is it considered a general attribute of an individual.Entrepreneurial potential (or predictability), as defined herein, isseen as a complex set of personal factors, including capabilities, thematching of these personal characteristics with a specific businessopportunity and with the social capital that an entrepreneur has accruedwithin a specific community of operation. The thesis of this technologyincludes the notion that the matches between all of these factors can bedeveloped and improved with conscious attention and training of anindividual. Furthermore, some embodiments of the present technology donot presume that there is a single ideal of entrepreneurship nor does itpresume that there is a single ‘anti-ideal’ of entrepreneurship, so theresulting scoring models are not limited to a single dimension ofreference.

Broadly, the present technology provides methods and systems forcapturing as many of a plurality of types of information aboutentrepreneurs and their communications as possible (especiallyelectronic data gathered from emails, websites, forums, blogs, and soforth). The present technology also provides systems and methods forextracting measures and/or features of the information and thecommunications and links (e.g., social connections) made by theentrepreneur (or between the entrepreneur and other parties). Thepresent technology may also employ these measures (e.g., metrics) todevelop predictive models relating to entrepreneurship.

In some embodiments, the present technology can employ the createdmodels to generate scores that represent entrepreneurial success (e.g.,entrepreneurial potential) for individuals, opportunities, and socialnetworks. The present technology may also communicate these scores tointerested parties or back to the entrepreneur.

FIG. 1 is a diagram of a process for receiving various sources ofinformation, extracting relevant information and translating theextracted information so that it can be stored in data stores relatingto attributes of either the entrepreneur, the business opportunity or tothe social network and social capital of the entrepreneur. Each of thesources of information involves a specific process to extract therelevant fields to be stored. As more sources of information areincorporated into the extraction process, more specific data can beadded to the categorized data leading to a more complete set of relevantdata. This process can be facilitated using the system 405 of FIG. 4,described in greater detail below.

FIG. 2 is a diagram of a process for extracting features from thecategorized databases, providing these features to predictive models(either mathematically derived or qualitatively derived), which thenproduces scores relating to the entrepreneurial success in question. Thecategories of data presented are indicative of the general categoriesthat may be kept relative to an entrepreneur, a specific businessopportunity, social network of the individual, social capital of theindividual, or any combinations thereof.

FIG. 3 shows a scoring model with multiple idealized clusters ofbehavior. In scoring models of this type, the subject is compared tomultiple idealized targets and scored based upon the nearest idealizedcluster. Guidance is given by suggesting to the subject behaviors thatwould make the subject's behavior correspond more closely with one ormore of the idealized behavior clusters.

FIG. 4 illustrates an exemplary architecture for practicing aspects ofthe present technology. The architecture comprises a businesstransaction analysis system, hereinafter “system 405” that is configuredto provide various functionalities, which are described in greaterdetail throughout this document. Generally the system 405 is configuredto communicate with client devices, such as client 415. The client 415may include, for example, a Smartphone, a telephone a laptop, acomputer, or other similar computing and/or communication device. Anexample of a computing device that can be utilized in accordance withthe present technology is described in greater detail with respect toFIG. 8.

The system 405 may communicatively couple with the client 415 via apublic or private network, such as network 420. Suitable networks mayinclude or interface with any one or more of, for instance, a localintranet, a PAN (Personal Area Network), a LAN (Local Area Network), aWAN (Wide Area Network), a MAN (Metropolitan Area Network), a virtualprivate network (VPN), a storage area network (SAN), a frame relayconnection, an Advanced Intelligent Network (AIN) connection, asynchronous optical network (SONET) connection, a digital T1, T3, E1 orE3 line, Digital Data Service (DDS) connection, DSL (Digital SubscriberLine) connection, an Ethernet connection, an ISDN (Integrated ServicesDigital Network) line, a dial-up port such as a V.90, V.34 or V.34bisanalog modem connection, a cable modem, an ATM (Asynchronous TransferMode) connection, or an FDDI (Fiber Distributed Data Interface) or CDDI(Copper Distributed Data Interface) connection. Furthermore,communications may also include links to any of a variety of wirelessnetworks, including WAP (Wireless Application Protocol), GPRS (GeneralPacket Radio Service), GSM (Global System for Mobile Communication),CDMA (Code Division Multiple Access) or TDMA (Time Division MultipleAccess), cellular phone networks, GPS (Global Positioning System), CDPD(cellular digital packet data), RIM (Research in Motion, Limited) duplexpaging network, Bluetooth radio, or an IEEE 802.11-based radio frequencynetwork. The network 420 can further include or interface with any oneor more of an RS-232 serial connection, an IEEE-1394 (Firewire)connection, a Fiber Channel connection, an IrDA (infrared) port, a SCSI(Small Computer Systems Interface) connection, a USB (Universal SerialBus) connection or other wired or wireless, digital or analog interfaceor connection, mesh or Digi® networking.

The system 405 generally comprises a processor 430, a network interface435, and a memory 440. According to some embodiments, the memory 440comprises logic (e.g., instructions or applications) 445 that can beexecuted by the processor 430 to perform various methods. For example,the logic may include a user interface module 425 as well as a dataaggregation and correlation application (hereinafter application 450)that is configured to provide the functionalities described in greaterdetail herein.

It will be understood that the functionalities described herein, whichare attributed to the system 405 and application 450 may also beexecuted within the client 415. That is, the client 415 may beprogrammed to execute the functionalities described herein. In otherinstances, the system 405 and client 415 may cooperate to provide thefunctionalities described herein, such that the client 415 is providedwith a client-side application that interacts with the system 405 suchthat the system 405 and client 415 operate in a client/serverrelationship. Complex computational features may be executed by thesystem 405, while simple operations that require fewer computationalresources may be executed by the client 415, such as data gathering anddata display.

In general, the user interface module 425 may be executed by the system405 to provide various graphical user interfaces (GUIs) that allow usersto interact with the system 405. In some instances, GUIs are generatedby execution of the application 450 itself. Users may interact with thesystem 405 using, for example, a client 415. The system 405 may generateweb-based interfaces for the client.

In some embodiments the system 405 may be configured to derive a score(or set of scores) that can be used to predict entrepreneurial behaviorand success-potential of a Business Person based upon informationcollected from any of: a Business Person, about the Business Person fromthird party sources, individuals in contact with the Business Person,social networks of the individual, and other information sources thatcan yield information relating to or are indicative of theentrepreneurial behavior of the individual. These scores are used withinthe context of a potential business transaction, such as the sale of abusiness or extending of a loan to an individual for a business purpose.

In some embodiments the system 405 is configured to extract informationabout entrepreneurial potential of a Business Person from socialnetworks and other data. For example, the system 405 may be configuredto link with various sources such as Facebook™, Linkedin™, Twitter™, andso forth, using an application programming interface (API).Alternatively, the system 405 may scrape web pages or social networkfeeds for necessary information.

In some embodiments, the system 405 is configured to calculate a levelof influence that a Business Person's social relationships will exertover the contracts entered into between or among the Business Person andother parties, such as investors. For example, the system 405 candetermine a number of business contacts for an individual, the relativeinfluence of each of these contacts, and a nature of relationshipbetween the individual and their contacts. By example, the system 405may score a relationship higher where the contact is highly influential,if the individual is in a very close relationship with the contact.Conversely the system 405 may score a relationship lower where thecontact is highly influential, if the individual is only casuallyconnected to the contact.

In some embodiments, the system 405 is configured to detect progress inthe entrepreneurial development of individual Business Persons basedupon their electronic communications such as emails, SMS messages,social network posts, and so forth.

In some embodiments, the system 405 is configured to provideproscriptive advice to Business Persons seeking to improve theirentrepreneurial capabilities by measuring and suggesting changes totheir electronic communications. For example, the system 405 may processemails of an individual and identify the vocabulary used in emails thatmay positively or deleteriously affect the business purposes of theBusiness Person. For example, if the system 405 detects poor grammarusage or typos in an individual's emails, the system 405 can instructthe individual in how to properly proofread their communications.

In some embodiments, the system 405 is configured to electronicallyreceive data relating to a Business Person's set of social network datawith information about various individuals to whom the Business Personis in contact. The system 405 is further configured to receive datarelating to the date, time, frequency and length of communicationmessages between a Business Person and other individuals.

In other embodiments, the system 405 is configured to append additionaldata to the communication information relating to the Business Person sothat social status and geographic information about the Business Personand individuals with whom the Business Person is in contact is collectedor extrapolated for use by the system 405.

In additional embodiments, the system 405 is configured to incorporategeographic-specific data relating to social, economic, demographicinformation into the data processing system; a system for communicationbetween Business Persons whereby they attain an electronic history ofparticipation in discussions about business topics.

In accordance with the present disclosure, the system 405 is configuredto crowdsource (or use crowdsourced) information, whereby a knowncommunity of Business Persons provides assessment of the quality andcontent of communications by a Business Person. The system 405 can alsocombine electronic information from a plurality of sources so as toprovide a score or scores that relate to various facets of the BusinessPersons such as their business skills, abilities, probability ofbusiness success, likelihood of completion of business goals, likelihoodof future business development and likelihood of various investmentreturns that may be relevant to potential investors. The system 405 cancreate a single score that represents any combination of theaforementioned facets. In other instances, several scores may becalculated and correlated to one another. For example, the system 405may generate one score for probability of business success, as well as asecond score that represents likelihood of future business development.

FIG. 5 illustrates an example method that can be executed by the system405 of FIG. 4. The method comprises the system 405 obtaining 505entrepreneur data related to a plurality of facets of an individual.Examples of facets comprise personal skills data, business history data,and social network data. In some embodiments, entrepreneur data can begathered across a plurality of network modalities.

In some embodiments, the system 405 collects information from severalnetwork modalities such as Facebook™, LinkedIn™, Google+™ phone records,SMS text records, e-mail meta-data, and so forth. The system 405 canexamine the depth of engagement between a target individual and theircontacts across these various modes of social connectedness. The system405 is configured to examine how many different modalities are used,recency of contacts, and the temporal elements of change in engagementwith each contact, especially those related to ‘business events’identified by the target individual.

To be sure, each of these data features are important on their own, butthe cross-modality aspect provides advantages and information about thetarget individual that would be impossible to obtain from a singlefeature analysis, or a plurality of individual features that are notcorrelated in a cross-modality analysis.

By way of example, as a business relationship is formed, contact withcertain individuals increases as deal parameters are discussed. Thosecontacts may initially begin as an e-mail introduction, leading to anumber of phone conversations, leading to more e-mails, leading to aconnection via LinkedIn and other social media networks. The change inthe number of connection points, the frequency and intensity of contact,and so forth is a dynamic measurement of engagement between individuals.

In some embodiments, the plurality of network modalities comprisessocial networks, phone records, and message records—just to name a few.

In more detail, the personal skills data comprises data surrounding theindividual. This process involves the ability to find and accesstargeted entrepreneurs and to gather data from and about thoseindividuals, their interests, their skills and their activities. Withrespect to business history data, the system 405 can obtain datasurrounding the business of the entrepreneur, which includes gatheringdata about business history, about specific business opportunitiesgenerated by the entrepreneur, about transaction structures employed—orable to be employed—in the execution of those business opportunities,and the collection of actual execution statistics for their businesses.

The social network data can comprise data that relates to the socialnetwork of the entrepreneur and their business activities, theconnections to people and entities, the frequency and intensity ofcontact and communication, and even the sequence of communications.Additional details regarding each of these types of data will bedescribed below with reference to a feature extraction process.

According to some embodiments, the method can include the system 405extracting 510 features from the personal skills data, business historydata, and social network data. To be sure, while a wide variety ofinformation is gathered pertaining to personal skills data, businesshistory data, and social network data, the system 405 is configured toparse this data out into facets that can be used in transaction relatedprocesses, as described below.

In some embodiments, the system 405 collects information (e.g.,entrepreneur data) using electronic data gathering techniques and storesthe information as unstructured data.

The following paragraphs relate to feature/facet extraction processes.One example feature extraction is experience. The system 405 isconfigured to evaluate numerical and textual indicators of experiencethat are gathered from social network sites to create an experienceindicator. Information used can include years in workforce, number ofemployers, positions held, skills enumerated by friends, pressreferences to individuals resulting from search-engine queries.

Another feature relates to education. The system 405 will evaluate theentrepreneur data for indications of degrees earned, educationalinstitutions attended, certificates of accomplishment or references totraining attended as well as other indicators of affiliation withinstitutions of education.

Another example feature is geographical footprint. In some instances,social media platforms provide geo-coordinate information (e.g., of lastlogin location) and textual clues (e.g., geographic references,home-town, city, state, country) that allow inferences to be made aboutan entrepreneur's footprint—or areas that are frequented by theindividual. This geographical information, coupled with developmentinformation about the areas frequented (e.g., income per capita, GDP,demographics, general development indicators) allows inference aboutopportunities to which the entrepreneur has been exposed. Greatergeographic exposure (based upon number of regions or continents orstates) and economic exposure (based upon development measures) providefor inference into the breadth of experience of the entrepreneur.

Another example feature includes geographical distribution ofcontacts/friends. To be sure, just as the geographical footprint of theentrepreneur can be measured, several geographic markers are availablefor most of the contacts in the entrepreneur's social networks. Not onlycan the extent of the geographic reach of friends be measured, but thedistribution into continent, country, region, and so forth be exploredand evaluated by the system 405. Additional data such as income, GDP,demographics, technical development indices, political measures provideadditional information on the ‘richness’ or variety of friendrelationships of the individual. The system 405 can categorize anindividual's relationships, for example, by region, by economicdevelopment of location, and so forth, and distributions of categorizedfriends and reach across physical space and economic distance factorinto diversification measures.

Another feature that can be extracted by the system 405 comprisesfunctional distribution of contacts. To be sure, just as contacts can becategorized by the system 405 based upon geography, the e-mail addressesof friends (or the domains of such e-mail addresses) provide indicationof function. For example, many e-mail addresses of contacts emanate fromdomains with free carriers like ‘gmail.com’ or ‘yahoo.com’ whichindicate private or connections that are personal rather thaninstitutional relationships. Other e-mail addresses have domains thatare institutional in nature (e.g., bob.smith@jpmorgan.com orjohn.doe@savethechildren.org). The system 405 searches the domain ofthese e-mails via text analytics and classifies these contacts intovarious groupings (e.g., banking, government, political, NGO, religious,and so forth). The system 405 then evaluates a distribution of theclassified e-mail contacts for each entrepreneur for diversification andfor indicators of breadth.

In some embodiments, the system 405 can evaluate features related tosocial network messages for the individual. In some embodiments, thesystem 405 analyzes and categorizes social network messages on a socialnetwork feed for an individual into clusters. For example, some messagesare mundane such as “I just ate a ham sandwich.”, while some relate tocurrent events “Rioting in streets.”, and some relate to professionalactivity “New article on prescribing app in Pharmaceutical Journal” ortechnology issues “Where do we go now on Net Neutrality?”. Messages canbe categorized by the system 405 for the entrepreneur, and similarlycategorized for the friends/contacts (followed/followers) of theindividual. The system 405 determines the distribution of categorizedfeeds which provides measures for diversification, breadth and‘seriousness’ of the individual.

In one embodiment, the system 405 uses a feature such as referrals. Thesystem can detect and collect a referral network of entrepreneurs that,once they register with the system 405, refer other individuals to thesystem 405. Such referrals indicate a form of influence that is measuredby the system 405. The quality of the person responding to the referralreflects on the status of the referring party.

In another feature, the system 405 can analyze phone records for theindividual. The system 405 enables individuals to provide the system 405with access to their phone records, for example by sending scannedimages of their cell-phone records and/or by permitting the system 405access to their phone-logs on their mobile devices. The system 405utilizes time, duration and contact information from these logs todetermine which contacts are current, who originates contact, what isthe sequence of contact (e.g., following a call with a first contact acall is made to a second contact), what is the duration of contact(short message or long conversation), what is the frequency of contact,what is the time of day for contact and other similar events. The callinformation provides insight into the dynamic nature of the socialnetwork structure of the individual.

In some embodiments, the system 405 can also analyze SMS/MMS records ina manner that is similar to phone conversations. Additionally, thesystem 405 can also analyze email messages and email metadata from ananalysis of email history. The system 405 can examine a frequency, levelof engagement, and other similar measures as referenced above with thephone and SMS records. The system 405 can identify clusters of contactsthat appear in groupings (cc or bcc records) of e-mail addresses. These,together with the other information that the system 405 gathers aboutthe contacts provides the system 405 with category distributions andlinkages between individuals that allow great insight into the dynamicaspects of the social network of the individual.

The previous paragraphs represent data collection and data processingtasks executed by the system 405. By layering the modalities of contactand examining the process of deepening the engagement with individualsacross linkage modes the system 405 provides unique insight into theentrepreneurial ability of a target individual.

To be sure, these extracted entrepreneur data types can be used invarious predictive scoring methodologies, as well as businessopportunity analyses that utilize these predictive scores.

In some embodiments, the method includes the system 405 determining 515business event information for business events identified between theentrepreneur and contacts of the entrepreneur found in the entrepreneurdata.

Business event information includes various types of information aboutbusiness ventures that the target individual participated in. Forexample, the system 405 can determine historical business informationthat relates to income, expense and business growth by date such ascategories of sales, cost of goods, fixed and variable expenses, and soforth. This information is maintained to provide insight into thestability of the business operated by the target individual and toenable us to determine the stability and risk-factors associated withthe business. Certain ‘common-size’ analyses such as dividing expensesby sales to obtain measures like ‘labor per dollar of sales’ allow thesystem 405 to combine many similar companies into categories to identifyoutliers. Additionally, the area of ‘statistical process control’provided by the system 405 provides a suite of analyses that identifybusiness elements that are ‘out of control’—or that vary in ways thatshould raise alarm. The system 405 can identify and categorize businessrisks using fixed versus variable expense analysis to determine businessbreak-even points.

In some embodiments, the entering of business data into the system 405by the target individual is viewed as an indicator of the individual'sdiligence in reporting. The extent and regularity of the businessreporting provides a measure of the individual's capabilities incommunicating financial information and general ‘bankability’ of theindividual.

In addition to collecting general business information, the system 405is configured to allow the individual to enter sales amount, deliverydate, invoicing date and collection date for their customers. Thisinformation provides for customer-by-customer scrutiny of paymentpatterns and potential payment delays by the system 405. From paymenthistory information the system 405 can establish expected payment timingthat relate to future transactions.

In some embodiments, the system 405 is adapted to maintain a set ofdesirable business behaviors that are used to assess the cross modalityset of entrepreneurial data obtained as described above.

Examples of non-limiting examples of desirable business behaviorsinclude business knowledge, capability within industry, communicationability, trust, relationship value relative to other individuals in thesystem 405, compliance, reliability, integrity, follow through, andresponsiveness—just to name a few.

In some embodiments, the system 405 identifies indicators of thesedesirable characteristics and maintains estimates of relative strengthfor each individual.

In one example, a length of time between the sending of an e-mail queryto an entrepreneur and receiving the response might figure into the‘score’ relating to communication ability, value, reliability, followthrough and responsiveness. The entrepreneur's ability to respond tobasic business questions, such as asking them to categorize last-month'sbusiness expenses into fixed vs. variable costs might figure into the‘score’ relating to knowledge and compliance. Each query or interactionwith the system 405 that comprises a part of the individual andinformation gathering relationship can be utilized by the system 405 in‘scoring’ of the individual along these attributes (e.g., facets). Theassessment of the individual along these dimensions is dynamic and isexpected to change as their relationships develop.

In some embodiments, the method includes analyzing a proposedtransaction for the individual. In one embodiment, this analysisincludes performing 520 a dynamic measurement of engagement between theentrepreneur and the contacts by looking for contacts between theentrepreneur and the contacts that cross the plurality of networkmodalities. To be sure, the dynamic measurement comprises at least oneentrepreneur score for the entrepreneur. The entrepreneur score is across-modality score that can be calculated in a multi-loci modelingprocess, which is described in greater detail below.

As mentioned above, the capturing of entrepreneur data and extraction offeatures can continue even during the performance of a transaction(e.g., business opportunity) between the target individual and one ormore parties. To be sure, the method can include the system 405analyzing business transactions to determine an individual's currentbusiness behaviors during a business opportunity.

For example, as business transactions unfold, certain events associatedwith the business transactions require attention and fact reporting. Forexample, if a party provides financing that might involve some goodsbeing shipped to an address in Kigali for use by an individual, theparty might require that the entrepreneur photograph the goods at theport and upload the photo. This trail of business facts provides a verysound basis for evaluating the seriousness of the individual relative tothe business opportunity. In some embodiments, the short-term nature oftrade-finance obligations financed by a party for an individual providesa ready measure of compliance. In fact, an entire communication chainrequired for a transaction provides a test of entrepreneur willingnessto comply—which is every bit as worthwhile as a stream of loan payments.Thus, the system 405 can continually monitor the individual's responsesand behaviors to a financing party's requests for information andperformance. The system 405 can maintain a script of expected behaviorsfor the individual and compare their actual performance to the script ofexpected behaviors. In this way, the system 405 can deduce compliancewith the terms of the business opportunity and assess deviations fromthis expected behavior.

Also, the system 405 can gather actual transaction risk metrics. Forexample, the system 405 can determine the actual variations in paymentamount, timing, and so forth for purchaser type and for product type.The system 405 can also determine, for example, which suppliers haveconsistent quality based on rejection rates, based on industry orproduct type, or based on other factors that would be apparent to one ofordinary skill in the art with the present disclosure before them.

Referring now to FIG. 6, another example method for iterative scoringand entrepreneurial evaluation is illustrated.

In an initial step 605, data is gathered as provided in the examplesabove. This data can comprise any of the entrepreneur data describedherein. Next, the method includes a step 610 where features areextracted from the entrepreneur data.

An initial score (K_(i)) is calculated in step 615. Example K scorecalculations are described in greater detail throughout this disclosure.

To be sure, if insufficient entrepreneur data exists in the system, thesystem can collect more data, routing back to step 605. If sufficiententrepreneur data exists then the method proceeds to step 620 where thesystem can evaluate if the score K_(i) is sufficient to move towardfunding a transaction (e.g., business opportunity). Thus, the system canmaintain scoring thresholds for a transaction. If the score calculatedfor the individual does not meet or exceed this threshold, the systemcan identify the transaction as incompatible. The system can identifythose aspects or facets that contributed to the low score and providesuggestions that would, if implemented by the individual, cause theirscore to rise above the score threshold.

It will be understood that each transaction type might require differingamounts of entrepreneur data for a complete analysis of the transaction.Thus, the system can be configured to periodically determine, at eachanalysis step, if sufficient entrepreneur data exists to make aninformed decision.

If the entrepreneur has a sufficient score (K) to pass the threshold,the system can then collect 625 information on transaction andultimately determine 630 if the transaction is worthy of funding.

In some embodiments, the system can make multiple attempts to match theentrepreneur with a business opportunity if other opportunities are nota match.

In some embodiments a suitable business opportunity is found by thesystem and the system can cause 635 the transaction to be funded.

As mentioned above, the system can assess 640 entrepreneur behaviorduring transaction execution. The system can add 645 entrepreneurbehavior during or after a transaction, or potentially after deficiencyis detected. For example, the system can determine that the individualmissed a milestone payment or the individual failed to prepare a reportor assessment on time.

This new information is added to the system and a ‘new’ score(K_((i+1))) is calculated in step 650. At each iteration, as new dataare added, the score is continually evaluated to determine if theentrepreneur, business and social network of the entrepreneur meritproceeding with the business transaction proposed by the entrepreneur.

Rapid recalculation of scores to incorporate new social data, newbehavior data and new business data provides advantages such as quickidentification of business opportunities/transactions that are in dangerof failing. Thus, the funding party need not wait until a transactionbecomes unsalvageable to mitigate their losses and fix transactionrelated issues.

As mentioned above, the present technology provides advantages overother scoring models, such as are used for credit scores. These simplemodels typically identify a targeted ‘ideal’ customer type, such asthose that repay loans fully and on time, and the ‘non-ideal’ customersuch as those that do not repay a loan fully. Such a process usesmathematics to create a linear equation based on several measureableattributes of the customer population that provides ‘maximum’ separationof the two customer types. This linear scoring model is often based uponlinear ‘discriminant analysis’ or some variant thereof. Once a scoringmodel is ‘built’ one simply uses the model to obtain a score for eachindividual. The scoring of an individual was a low-computing resourceactivity that could be achieved by hand. These processes used highinitial reliance on computing and statistics at model build time, butlow reliance on computing and statistics at individual assessment time.While linear discriminant analysis is simple and easy to understand, itoften is not the ideal methodology for ‘scoring’ individuals in manycircumstances.

Major criticisms regarding these linear methodologies have to do withthe heterogeneity of the two types of individuals being evaluated. Theremay be a great variety of reasons why people do not pay loans, forexample—suggesting that there is not one single ‘type’ of non-payingcustomer, but many types. Similarly, there may be many types of ‘paying’customers. So, instead of drawing a line from the centroid of one typeof individual to the centroid of the other (which is the essence oflinear discriminant analysis), clustering of customers into varioustype-groupings is employed by the present technology.

To be sure, the present technology employs multi-loci modeling thatdiffers from traditional linear scoring in that there is no singlelinear discriminant function that provides a single scoring ‘line’ inthe entrepreneur attribute space. Instead, individuals are grouped basedon a weighting of their attributes (e.g., individual features or a setof features). Weightings are used to create these clusters are selectedto maximize the variation in customer group measurements (e.g., loanrepayment) on a group-by-group basis. Customer group measurements arealso referred to herein as “desirable business behaviors”.

The attribute weightings that provide the greatest variation incustomer-cluster performance are identified by the system 405. When atarget individual is evaluated, that target individual is compared tothe centroid of a plurality of clusters of other individuals. The targetindividual is scored relative to its ‘distance’ to the nearest, bestperforming cluster. To be sure, distance in this instance is theattribute-weighted measures used to optimize the clustering. In otherwords, the individual is not compared to the single centroid of allideal individuals—as in linear discriminant analysis—but rather iscompared to the nearest, best centroid of successful individuals thatare most like this target individual. This approach uses a high-level ofcomputing resources and statistical power at the initial time of modelbuilding, but it also uses a high-degree of computing and statisticalanalysis at the time that each individual is evaluated.

To be sure the ‘ideal individual/entrepreneur’ is based on anexpectation of entrepreneurial success, not simply of a linear analysissuch as with credit assessment predicting loan repayment.

Using the methodology provided above, the present technology can includea method that is executed by the system 405, as illustrated in FIG. 7.In some embodiments, the method can comprise obtaining 705 for pluralityof individuals, entrepreneur data related to personal skills data,business history data, and social network data for the entrepreneuracross a plurality of network modalities.

Once the data has been obtained, the method includes extracting 710attributes from the entrepreneur data and building 715 a database ofunstructured data from the attributes.

Next, the method includes analyzing a target individual against thedatabase using a multi-loci modeling process. In some embodiments, themulti-loci modeling process comprises applying 720 attribute weightingsto each of the attributes extracted for the individuals. Next, themethod includes grouping 725 the individuals into customer clusters insuch a way that a variation between individuals is maximized relative toa group business measurement.

In some embodiments, the method includes calculating 730 a centroid ofeach of the customer clusters and comparing 735 a target individual tothe customer clusters.

Finally, the method includes determining 740 a best performing clusterfor the target individual. In some embodiments, the best performingcluster is a customer cluster of the customer clusters with a shortestdistance between the target individual and the customer cluster. Anillustration of a multi-loci analysis is provided in FIG. 3.

FIG. 8 illustrates an example flow diagram that can be implemented in aspecific purpose computing device, such as the system of FIG. 4. In someembodiments, data are initially aggregated from a Mobile App 802installed on a mobile device such as a smart phone, or from a Web Appapplication 804 available to the User over the Internet. Both of thesesystems communicate with a Go-lang API 806 accessible via an Internetaddress. Once this API has been activated, it then initiates a series ofactions on multiple machine clusters within a computing “cloud.”

Each ellipsoid in this diagram identified as “SQS” represents amessaging queue that signals to yet another computer or cluster ofcomputers to initiate the next process described. For example, theGo-lang API 806 initiates a process Get Gigya data 808—which is a thirdparty aggregator of FaceBook™ LinkedIn™ and Twitter™ data (as well asother social-media data). These data are collected and stored to adatabase, but several other processes on several other computer clustersare initiated. These processes, in turn, spawn other processes, whichwhen all are completed, result in several types of data having beenstored with respect to the User who engaged with the Mobile or Web App.

For example, the system can include a Receive Mobile data module 810that receives SMS messages and call logs from the mobile device (as wellas other communication types), a Receive Email module 812 that receivesemails from email accounts associated with an individual, and a ReceiveUser query data module 814 that obtains data about the individual fromvarious electronic resources such as data repositories, social networks,websites, and similar resources.

Data Reduction Through Feature Identification

In addition to these data collection steps, additional processes aretriggered that scan the data resulting from the above-described process.These other processes extract features from the large volume ofresulting data. Features can be extracted in a feature extraction layer816. The system can employ a plurality of feature extractors to extractemail domains, social network information, names, and so forth.

For example, a feature entitled “Experience” might be extracted fromthese data using a number of data elements. Specifically, the dates ofemployment associated with an individual might be noted from the datarecords obtained, together with the job titles. These are oftenavailable from aggregated data from social media sites. In oneembodiment, experience score values result from the aggregate number ofyears worked within an industry.

Additionally, a search engine query can be triggered using theindividual's name and country (or company, or city, or profession) andthe results returned by the search engine are stored. If the detailsfrom the returned pages match the details of the individual in theenquiry, then certain context information is extracted. The source ofthe information is extracted (Was this a ‘news’ source? An ‘industry’publication? A conference proceeding? An NGO publication?, and soforth). Based upon the number and nature of the web-based references forthis individual, the scoring process assigns a numerical value to thisindividual. If they appear to be a high-profile person with numerousquotations and references in industry magazines or conferenceproceedings, for example, then it can be presumed that the individualhas a high degree of experience and credibility. If no web referencesare found (or if the only references are self-generated via profileinformation supplied by the individual to sites such as LinkedIn), thenthat individual would have a much lower experience score.

The system can utilize a plurality of search engines and data scrapers818 to obtain additional information using the extracted featuresdetermined in the feature extraction layer 816.

In some embodiments, the system can utilize a correlation process 820 tomatch extracted names, emails, phone records, and other extractedentrepreneur data to a specific person or node (entity, business, and soforth).

Scoring Use Case

Provided below is a non-limiting example of a scoring process thatutilizes several extracted features. These scores indicate some of thepotential measures used in calculating a k-score (K). The variable “REP”near the bottom of the TABLE 1 is an indicator of the type of scoringthat can be utilized to enhance the score of an entrepreneur thatensures all money is repaid—and that penalizes an entrepreneur that doesnot ensure all money is repaid. Each of these ‘variables’ in thisexample only totals a maximum of five points. The weighting of eachcomponent in a more sophisticated K entrepreneur score would besignificantly different due to the presence of many additional features.

TABLE 1 Education *ED: Score 5 pts Graduate degree, 4 pts Universitydegree, 3 pts some University, 2 pts High School, 0 no mentionExperience *EX: Add 1 point for each year of employment in related fieldto max 5 pts Skills *SK: Add .5 point for each relevant skill to max 5pts Authentication *AU: Score 1 point each modality authenticated to max3 pts, plus 1 point for phone & SMS, plus 1 point for e-mail WebPresence *WP: Add 5 points >3 web references, 4 points 3 references, 3points 1-2 references, 0 points no references Social Network *SN1: Add.1 point for each friend/contact with >3 web references to max 5 pointsSocial Network *SN2: Add .25 points for each friend/contact with >3 webreferences with whom contact <30 days to max 5 points Business Info*BI1: 5 points if No Explanation needed, 4 points Some Explanation, 3points Extensive Explanation, 2 points Don't Understand, 1 point notable, 0 points No Try Business Info *BI2: Score 1 point for each businfo item submitted to max 5, decays ½ pt per week Referrals *REF1:Score 1 point for each referral made that connects to Kountable, to max5 points Referrals *REF2: Score .25 points for each referral made to max5 points Repayment *REP: +5 points complete-timely repayment, −1 pointscomplete-non-timely repayment w/ legit excuse, −2 pointscomplete-non-timely repay w/o excuse but w/ effort, −3 points incompletepayment w/ effort, −5 points incomplete payment no effort Responsiveness*RES: Score 5 points if respond in <24 hours, 4 points <48 hours, 3points <72 hrs, 2 points <7 days, 1 point <30 days, 0 points >30 days

This specific example of scoring illustrates 13 specific features thatare scored in order to calculate one embodiment of a K_(i) score. In thecomplete scoring model there are hundreds of features extracted andscored. Continuous analysis adds additional ‘features’ to the model ateach development cycle. The features are quantitative representations ofinformation known about the individuals. A numerical evaluation processcontinuously examines the features available and identifies whichfeatures are most predictive of the behaviors that we desire to select.

Example of Weighting

There are, quite literally, an infinite number of ways to obtainweightings for the observed and measureable ‘feature scores’ that areused in getting the various K_(i) and subsequent K scores. The methodfor obtaining the weights that are used, however, generally follows theprocess defined below.

First, each individual (X_(i)) is represented by p feature measures. Inone embodiment, there are perhaps hundreds of such measures. An exampleequation is provided belowX_(i)={x_(i1),x_(i2),x_(i3), . . . ,x_(ip)}  Equation 1Generally, the system obtains measures from n individuals (n>p), thenconstructs a matrix X in accordance with Equation 2 below

$\begin{matrix}{X = {\begin{bmatrix}x_{11} & x_{12} & x_{13} & \; & x_{1p} \\x_{21} & x_{22} & x_{23} & \ldots & x_{2p} \\x_{31} & x_{32} & x_{33} & \; & x_{3p} \\\; & \vdots & \; & \ddots & \vdots \\x_{n\; 1} & x_{n\; 2} & x_{n\; 3} & \ldots & x_{np}\end{bmatrix}.}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

From this matrix X we can find up to p unique principal components (orEigen vectors). A principal component consists of a vector of weightsω_(i)={ω₁, ω₂, ω₃, . . . ω_(p)} and a measure λ_(i) (the Eigen valueassociated with the Eigen vector). Usually these Eigen vectors aresorted in descending order of their Eigen values and are called thefirst principal component, the second principal component, and so forth.The weights, ω_(i), for each principal component comprise an initial setof weights to apply to the measures X_(i) for each individual. In someembodiments, these weights, ω_(i), are usually further weighted by the‘information content’ of each of the principal components.

One measure of ‘information’ to use for weighting a principal componentmight be the ‘Shannon information index’ utilized in information theory.In this case, the information weighting would have to do with the‘randomness’ of the observations within that principal component. Forexample, if the ‘good entrepreneurs’ (each with its measures X_(i)) werecompletely disordered when plotted along that principal component, thenthe system would consider there to be little information in thatcomponent. If, on the other hand, all of the ‘good entrepreneurs’ wereclustered together (say at the high end of that component dimension),then the system would consider there to be a great deal of Shannoninformation in that component.

The system can then figuratively ‘plot’ the positions of theentrepreneurs in this ‘information-weighted’ principal component spaceand utilize those information/Eigen vector weights as Euclideancoordinates. Most frequently, only the first few (arbitrarilyfew—sometimes three, sometimes five, and so forth depending upon thefall-off of the information-weighted Eigen value curve) Euclideancoordinates are utilized.

Using a methodology similar to ‘k-means’ clustering, we cluster ‘goodentrepreneurs’ into small groups within this weighted space. The meanvalues of these clusters of ‘good entrepreneurs’ constitute centroidsfor our multi-loci measurements. Each potential entrepreneur is measuredagainst each of these ‘loci’ of ‘good entrepreneurs’ (i.e., a distancemeasure is calculated between the ‘location’ of the potentialentrepreneur in this weighted Euclidean space and the centroid of eachcluster of ‘good entrepreneurs’ in the same weighted space). The k-score(entrepreneur score) is, in reality, a measure of this distance of thepotential entrepreneur to the nearest centroid of a cluster of ‘goodentrepreneurs.’ An example scoring methodology of the presenttechnology, however, for historical reasons, uses an inverse measure ofdistance for the k-score. That is, a larger score represents a smallerdistance to a centroid. An example k-score, then, is in reality ameasure of ‘proximity’ to a centroid rather than a measure of distance.

In an example methodology summary, a system of the present technology isconfigured to obtain principal components of an entrepreneur data space.Next, the system will obtain information weightings for each of theprincipal component dimensions and rotate the entrepreneur data usingthe information-weighted principal component values. In someembodiments, the system can cluster ‘good entrepreneurs’ into smallgroups and measure the ‘distance’ between the potential entrepreneur andthe known centroids of ‘good entrepreneurs’. In some embodiments, thesystem can transform the distance measure to the nearest centroid into aproximity measure.

The actual principal component rotations and the actual weights utilizedin this analytical process are derived by the mathematical operationsdescribed above. As the number of measures applied to each entrepreneurincrease (which can increase as our experience grows), the mathematicsdetermine the scores as a result of applying this process to the data.

FIG. 9 illustrates an exemplary computing system 1 that may be used toimplement an embodiment of the present systems and methods. Thecomputing system 1 of FIG. 9 includes a processor 10 and main memory 20.Main memory 20 stores, in part, instructions and data for execution byprocessor 10. Main memory 20 may store the executable code when inoperation. The computing system 1 of FIG. 9 further includes a massstorage device 30, portable storage device 40, output devices 50, inputdevices 60, a display system 70, and peripherals 80.

The components shown in FIG. 9 are depicted as being connected via asingle bus 90. The components may be connected through one or more datatransport means. Processor 10 and main memory 20 may be connected via alocal microprocessor bus, and the mass storage device 30, peripherals80, portable storage device 40, and display system 70 may be connectedvia one or more input/output (I/O) buses.

Mass storage device 30, which may be implemented with a magnetic diskdrive or an optical disk drive, is a non-volatile storage device forstoring data and instructions for use by processor 10. Mass storagedevice 30 can store the system software for implementing embodiments ofthe present technology for purposes of loading that software into mainmemory 20.

Portable storage device 40 operates in conjunction with a portablenon-volatile storage medium, such as a floppy disk, compact disk ordigital video disc, to input and output data and code to and from thecomputing system 1 of FIG. 9. The system software for implementingembodiments of the present technology may be stored on such a portablemedium and input to the computing system 1 via the portable storagedevice 40.

Input devices 60 provide a portion of a user interface. Input devices 60may include an alphanumeric keypad, such as a keyboard, for inputtingalphanumeric and other information, or a pointing device, such as amouse, a trackball, stylus, or cursor direction keys. Additionally, thesystem 1 as shown in FIG. 9 includes output devices 50. Suitable outputdevices include speakers, printers, network interfaces, and monitors.

Display system 70 may include a liquid crystal display (LCD) or othersuitable display device. Display system 70 receives textual andgraphical information, and processes the information for output to thedisplay device.

Peripherals 80 may include any type of computer support device to addadditional functionality to the computing system. Peripherals 80 mayinclude a modem or a router.

The components contained in the computing system 1 of FIG. 8 are thosetypically found in computing systems that may be suitable for use withembodiments of the present technology and are intended to represent abroad category of such computer components that are well known in theart. Thus, the computing system 1 can be a personal computer, hand heldcomputing system, telephone, mobile computing system, workstation,server, minicomputer, mainframe computer, or any other computing system.The computer can also include different bus configurations, networkedplatforms, multi-processor platforms, etc. Various operating systems canbe used including UNIX, Linux, Windows, Macintosh OS, Palm OS, and othersuitable operating systems.

Some of the above-described functions may be composed of instructionsthat are stored on storage media (e.g., computer-readable medium). Theinstructions may be retrieved and executed by the processor. Someexamples of storage media are memory devices, tapes, disks, and thelike. The instructions are operational when executed by the processor todirect the processor to operate in accord with the technology. Thoseskilled in the art are familiar with instructions, processor(s), andstorage media.

It is noteworthy that any hardware platform suitable for performing theprocessing described herein is suitable for use with the technology. Theterms “computer-readable storage medium” and “computer-readable storagemedia” as used herein refer to any medium or media that participate inproviding instructions to a CPU for execution. Such media can take manyforms, including, but not limited to, non-volatile media, volatile mediaand transmission media. Non-volatile media include, for example, opticalor magnetic disks, such as a fixed disk. Volatile media include dynamicmemory, such as system RAM. Transmission media include coaxial cables,copper wire and fiber optics, among others, including the wires thatcomprise one embodiment of a bus. Transmission media can also take theform of acoustic or light waves, such as those generated during radiofrequency (RF) and infrared (IR) data communications. Common forms ofcomputer-readable media include, for example, a floppy disk, a flexibledisk, a hard disk, magnetic tape, any other magnetic medium, a CD-ROMdisk, digital video disk (DVD), any other optical medium, any otherphysical medium with patterns of marks or holes, a RAM, a PROM, anEPROM, an EEPROM, a FLASHEPROM, any other memory chip or data exchangeadapter, a carrier wave, or any other medium from which a computer canread.

Various forms of computer-readable media may be involved in carrying oneor more sequences of one or more instructions to a CPU for execution. Abus carries the data to system RAM, from which a CPU retrieves andexecutes the instructions. The instructions received by system RAM canoptionally be stored on a fixed disk either before or after execution bya CPU.

Computer program code for carrying out operations for aspects of thepresent technology may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

In general, the present disclosure is related to secure communicationsmethods for use with entrepreneurial prediction systems and methods suchas those described infra. An example method can include the use of twofactor authentication of both a communications channel used by theentrepreneur (either by device or message attributes) and anidentification of the entrepreneur from biometric parameters. Thisallows for secure communication with an entrepreneur when theentrepreneur is communicating from a geographical location of low trust,such as where device or identity theft is common.

Stated otherwise, when communicating with individuals in some marketswhere societal trust is low and fraud-related crime rates are high,written or telephonic communications are highly suspect.

When financial information, such as bank account information, iscommunicated from such locales via written electronic media (such ase-mail or SMS text), there is a sufficiently great probability that suchcommunication is fraudulent that secondary contact, such as using avoice or video call, is required. This creates friction and slows downnormal business communication processes, particularly at scale.

In some embodiments, the present disclosure involves an automatedtwo-factor verification procedure. One facet of the two-factorverification procedure is verification of a communication channel. Asecond facet of the two-factor verification procedure is verification ofa message sender (e.g., a target entrepreneur). Additional details onaspects of these two factor processes are described in greater detailbelow.

FIG. 10 illustrates an example system that can be utilized in accordancewith the present technology. The system can include a communicationsdevice 1015 such as a Smartphone or laptop that is utilized by a targetentrepreneur. The communications device 1015 can include an entrepreneurevaluation application 1010 (entrepreneur application 1010) that allowsthe communications device 1015 to access the systems and methodsdescribed above.

The system includes a data processing system 1005 that comprises anauthentication application 1025 that is used to implement theauthentication methods which are described in greater detail herein.Additionally, the data processing system can include any of thecomponents of system 450 of FIG. 4.

The communications device 1015 can couple with the data processingsystem 1005 over a communications network 1020 via a communicationschannel 1030. The type of network used can vary according to the type ofdevices utilized. For example, the network can include atelecommunications network if the communications device 1015 is aSmartphone.

The processes can begin with the collection of additional types ofentrepreneur data, which are different than the entrepreneur datacollected in the previously described embodiments. Thus, in addition topersonal skills data, business history data, and social network data,the entrepreneur data can comprise data such as location information andbiometric information for an entrepreneur, as well as identifyinginformation for a communications device 1015 used by the entrepreneur toaccess the communications network 1020.

These various types of information can be pre-stored in an entrepreneurdatabase 1035 and can be used as a benchmark or baseline forauthentication processes.

In one embodiment, a first of the two factor authentication process caninvolve verification of the communication channel 1030. For context,when a secure message is sent to or from the entrepreneur, there is aneed to ensure that the communication has not been modified or changeden route to its destination, such as the data processing system 1005 oranother end user computing system. An example method of ensuring thatthis message communication process has not been disrupted involves atleast one of the following processes. In one example process the dataprocessing system 1005 performs device authentication. The deviceauthentication process associates specific originating and receivingdevice identifiers (e.g., computing device identifiers) with an intendedcommunication source and destination. Each mobile communication devicethat communicates via mobile networks contains a SIM (subscriberidentity module) card that uniquely identifies the sending or receivingdevice. Devices that communicate via the Internet contain hardwareidentifiers (such as MAC addresses). These identifiers can be associatedwith specific individuals prior to the secured communication during adevice registration process. That is, specific devices can be associatedwith specific individuals.

For example, when a target entrepreneur registers with the dataprocessing system 1005, the data processing system 1005 requestsidentifying information from the communications device 1015. In oneexample, the communications device 1015 executes the entrepreneurapplication 1010 that obtains required identifying information from thecommunications device 1015 and provides that identifying information tothe data processing system 1005.

In another example, message authentication can be used to ensure thatthe message communication process has not been disrupted. In oneembodiment a message from the target entrepreneur is encrypted by thedata processing system 1005 using pre-arranged device public and privatekeys.

In yet another embodiment the data processing system 1005 associates‘checksum’ measures using error detection blocks, for example. To besure, encryption helps to avoid spurious sniffing of message content.The checksum provides a means for verifying that the message has notbeen changed in transit.

A second stage or facet of authentication involves verification of anidentity of a message sender, such as the entrepreneur.

In one embodiment, the data processing system 1005 is configured toimplement biometric authentication. For example, either the dataprocessing system 1005 can verify an identity of a message sender and/ora message recipient. The data processing system 1005 pre-stores certainbiometric information about the party or parties to the communication.For example, upon registration with the data processing system 1005, amessage sender is asked to provide a clear facial photograph (e.g., a‘selfie’) and a clear photograph of one or more fingers that clearlyshows fingerprints. Biometric parameters are extracted from thesephotographs/image files. For example, fingerprint patterns or uniquelyidentifying facial patterns are identified. This biometric informationis stored in the entrepreneur database 1035.

At the request of a receiving party, a current image type is requested(photo of finger showing fingerprints or of the sender's face—a‘selfie’). The sending party will capture a current photograph withtheir communications device 1015.

The data processing system 1005 will process the current photograph byextracting biometric parameters (e.g., facial features) from the currentphotograph. The data processing system 1005 can then authenticate theuser by comparing the biometric parameters of the current photographagainst the pre-stored biometric information for the user.

If the biometric parameters extracted from the requested image matchthose that are pre-associated with the sender, then that message isadjudged to be biometrically authenticated. A recipient verifies theiridentity by sending an authenticated message in response to a request ina form of message handshaking.

In another process, the data processing system 1005 can utilizemeta-data authentication. For example, the data processing system 1005can extract time and GPS (Global Positioning System) ‘meta-data’parameters associated with a requested verification image. The requestedimage can contain meta-data that is commonly imbedded within digitalimage files created by mobile device photography. The timestampassociated with the image and the GPS coordinates of the image can becompared to pre-stored meta-data information for the sender toverify/authenticate the user when a match is determined.

For example, the data processing system 1005 can implement a timewindow/threshold of two minutes. If timestamp meta-data of the currentimage is not within two minutes, the message is rejected. GPS locationmeta-data extracted from the current image can be compared with a GPScoordinate range expected for the message sender. Again, the GPScoordinate range can be determined from location information obtainedfrom the sender when they registered with the data processing system1005.

The two factor authentication method (e.g., verifying the communicationschannel and the identity of the sender and/or receiver) when takentogether constitute a secure process for validating communicationbetween two individuals, particularly when one or more of the partiesare associated with a low-trust locale (e.g., where device or identitytheft may be common).

FIG. 11 is flowchart of a method for verifying secure communicationsbetween a sending party and a receiving party over a communicationsnetwork. In one example, the method includes a sending party such as atarget entrepreneur and a receiving party is an entrepreneur scoringsystem, such as the data processing systems described herein. In anotherexample, the sending party is a target entrepreneur and the receivingparty is another end user such as a company or individual who desires toenter into a business arrangement with the target entrepreneur. The dataprocessing system can act as an intermediary for ensuring thatcommunications between the target entrepreneur and the other end userare secure. As mentioned above, the method is advantageous whenfacilitating communications between parties when one of the parties isin a low-trust geographical location. For example, the party may belocated in a country or city where malicious behavior such as identitytheft or device theft occurs. To be sure, these methods are not limitedto such circumstances, but do provide advantages in these geographicalsituations.

The method includes a step of determining 1105 if the sending orreceiving parties are accessing the communications network from ageographical location of low trust. For example, a location of thecommunications device can be determined from inspecting GPS dataobtained from the communications device. As mentioned above, this canoccur by an application executing on the communications device whichgathers data from the communications device. Location information canalso be determined from the network, such as by triangulation of thecommunications device as it operates within the network.

Next, the method includes verifying 1110 a communications channel of thecommunications network by authenticating 1115 communications devicesused by the sending party and the receiving party or authenticating 1120a message transmitted from the sending party to the receiving partyusing an encryption scheme and an error detection block associated withthe message. Steps 1115 and 1120 can be utilized in combination orsingularly. To be sure, steps 1115 and 1120 are part of a first facet ofthe two factor authentication process that ensures that communicationsare safe and secure.

In a second portion of the two factor authentication process, the methodincludes verifying 1125 the sending party by receiving 1130 currentbiometric information for the sending party and authenticating 1135 thecurrent biometric information against pre-stored biometric informationfor the sending party to authenticate or reject the transmitted message.

In some embodiments, the method can include an optional step oftransmitting 1140 an authentication message to the sending party ifcurrent biometric information is authenticated.

The method also includes a step of transmitting 1145 the message to thereceiving party if the communications channel and the sending party areverified. Thus, if the two factor authentication process is successful,the message is delivered to the receiving party.

FIG. 12 is a method for verifying secure communications in conjunctionwith the entrepreneurial analysis methods described throughout thepresent disclosure.

In one embodiment, the method includes pre-storing 1205 entrepreneurdata that comprises location information and biometric information foran entrepreneur, as well as identifying information for a communicationsdevice used by the entrepreneur to access the communications network.

Next, the method includes receiving 1210 a message transmitted from theentrepreneur using their communications device to a receiving party. Themethod can include, for example, a current photograph or fingerprint ofthe sending party, as well as any content that the sending party desiresto communicate to their intended receiving party.

Prior to delivering the content of the message, the method includesverifying 1215 a communications channel of the communications networkused by the entrepreneur by authenticating 1220 the communicationsdevice used by the entrepreneur. In a second factor, the method includesverifying 1225 an identity of the entrepreneur by receiving 1230 (ordetermining from the message) current biometric information for theentrepreneur. That is, the current biometric information can be receivedas a part of the message to be delivered or in a subsequent requestprocess by the receiving party (or the data processing system).

The method includes comparing 1235 the current biometric informationagainst the pre-stored biometric information for the entrepreneur toauthenticate or reject the transmitted message.

According to some embodiments, the method can include encrypting 1240the message and associating the message with an error detection block.The secure message is provided to the receiver by transmitting 1245 themessage to the receiving party if the communications channel and theidentity of the entrepreneur are verified.

The receiving party can use a key to decrypt the encrypted message andcheck the error detection block with a checksum value.

It will be understood that the methods of FIGS. 11 and 12 can includeadditional or fewer steps than those illustrated in the flow diagrams.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present technology has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the technology in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the technology. Exemplaryembodiments were chosen and described in order to best explain theprinciples of the present technology and its practical application, andto enable others of ordinary skill in the art to understand thetechnology for various embodiments with various modifications as aresuited to the particular use contemplated.

Aspects of the present technology are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thetechnology. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the technology.As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

It will be understood that like or analogous elements and/or components,referred to herein, may be identified throughout the drawings with likereference characters. It will be further understood that several of thefigures are merely schematic representations of the present technology.As such, some of the components may have been distorted from theiractual scale for pictorial clarity.

While the present technology has been described in connection with aseries of preferred embodiment, these descriptions are not intended tolimit the scope of the technology to the particular forms set forthherein. It will be further understood that the methods of the technologyare not necessarily limited to the discrete steps or the order of thesteps described. To the contrary, the present descriptions are intendedto cover such alternatives, modifications, and equivalents as may beincluded within the spirit and scope of the technology as defined by theappended claims and otherwise appreciated by one of ordinary skill inthe art.

What is claimed is:
 1. A method for verifying secure communicationsbetween a sending party and a receiving party over a communicationsnetwork, the method comprising: determining if the sending or receivingparties are accessing the communications network from a geographicallocation of low trust, the geographical location of low trust comprisingany location where malicious behavior such as identity theft or devicetheft occurs, the geographical location of low trust being determinedfrom an inspection of global positioning system information ofcommunications devices used by any of the sending or receiving parties;verifying a communications channel of the communications network bytwo-factor authentication, a first factor of the two-factorauthentication comprising any of: authenticating the communicationsdevices used by the sending party and the receiving party; andauthenticating a message transmitted from the sending party to thereceiving party using an encryption scheme and an error detection blockassociated with the message; and a second factor of the two-factorauthentication comprising verifying the sending party by: receivingcurrent biometric information for the sending party comprising any of aphotograph of a face of the sending party, a fingerprint of the sendingparty, or a combination thereof, the current biometric informationcomprising biometric parameters that are compared to biometricparameters of pre-stored biometric information; authenticating thecurrent biometric information against the pre-stored biometricinformation for the sending party to authenticate or reject thetransmitted message; extracting meta-data from the current biometricinformation; and authenticating the meta-data of the current biometricinformation against pre-stored meta-data for the sending party toauthenticate or reject the transmitted message; and transmitting themessage to the receiving party if the communications channel and thesending party are verified.
 2. The method according to claim 1, whereinthe photograph is verified by comparing an image file timestamp to atimestamp threshold.
 3. The method according to claim 2, furthercomprising extracting location information from the image file as themeta-data.
 4. The method according to claim 3, wherein the image file isverified by comparing the location information of the image file toexpected location information for the sending party.
 5. The methodaccording to claim 4, wherein the expected location information for thesending party is obtained from the communications device of the sendingparty.
 6. The method according to claim 1, wherein the geographicallocation of low trust includes any location where device or identitytheft are common.
 7. A method for verifying secure communicationsbetween a sending party and a receiving party over a communicationsnetwork, the method comprising: determining if the sending or receivingparties are accessing the communications network from a geographicallocation of low trust, the geographical location of low trust comprisingany location where malicious behavior such as identity theft or devicetheft occurs; verifying a communications channel of the communicationsnetwork by: authenticating communications devices used by the sendingparty and the receiving party; or authenticating a message transmittedfrom the sending party to the receiving party using an encryption schemeand an error detection block associated with the message; and verifyingthe sending party by: receiving current biometric information for thesending party comprising any of a photograph of a face of the sendingparty, a fingerprint of the sending party, or a combination thereof;authenticating the current biometric information against pre-storedbiometric information for the sending party to authenticate or rejectthe transmitted message; extracting meta-data from the current biometricinformation; and authenticating the meta-data of the current biometricinformation against pre-stored meta-data for the sending party toauthenticate or reject the transmitted message; and transmitting themessage to the receiving party if the communications channel and thesending party are verified.
 8. The method according to claim 7, furthercomprising storing identifying information for the communicationsdevices, wherein the identifying information associates the sendingparty and the receiving party with their respective device.
 9. Themethod according to claim 8, wherein authenticating communicationsdevices used by the sending party and the receiving party comprisesverifying the identifying information of the communications device bycomparing received identifying information to pre-stored identifyinginformation.
 10. A method for verifying secure communications between asending party and a receiving party over a communications network, themethod comprising: receiving a message transmitted between the sendingparty and the receiving party; encrypting the message and associatingthe message with an error detection block; determining if the sending orreceiving parties are accessing the communications network from ageographical location of low trust, the geographical location of lowtrust comprising any location where malicious behavior such as identitytheft or device theft occurs; verifying a communications channel of thecommunications network by: authenticating communications devices used bythe sending party and the receiving party; and verifying the sendingparty by: receiving current biometric information for the sending partycomprising any of a photograph of a face of the sending party, afingerprint of the sending party, or a combination thereof;authenticating the current biometric information against pre-storedbiometric information for the sending party to authenticate or rejectthe transmitted message; extracting meta-data from the current biometricinformation; and authenticating the meta-data of the current biometricinformation against pre-stored meta-data for the sending party toauthenticate or reject the transmitted message; and transmitting themessage to the receiving party if the communications channel and thesending party are verified.
 11. A method for verifying securecommunications over a communications network, the method comprising:pre-storing entrepreneur data that comprises location information andbiometric information for an entrepreneur, as well as identifyinginformation for a communications device used by the entrepreneur toaccess the communications network; receiving a message transmitted fromthe entrepreneur using their communications device to a receiving party;verifying a communications channel of the communications network used bythe entrepreneur by: authenticating the communications device used bythe entrepreneur; and verifying an identity of the entrepreneur by:receiving current biometric information for the entrepreneur comprisingany of a photograph of a face of the entrepreneur, a fingerprint of theentrepreneur, party, or a combination thereof; and authenticating thecurrent biometric information against the pre-stored biometricinformation for the entrepreneur to authenticate or reject thetransmitted message; and encrypting the message and associating themessage with an error detection block; and transmitting the message tothe receiving party if the communications channel and the identity ofthe entrepreneur are verified.
 12. The method according to claim 11,further comprising: extracting meta-data from the current biometricinformation; and authenticating the meta-data of the current biometricinformation against pre-stored meta-data for the entrepreneur toauthenticate or reject the transmitted message.
 13. The method accordingto claim 11, further comprising: authenticating a current location ofthe communications device of the entrepreneur by comparing the currentlocation with the location information that is pre-stored for theentrepreneur, wherein the current location is authenticated if thecurrent location falls within a range of location coordinates relativeto the location information that is pre-stored.